Autonomous systems rely on artificial intelligence and machine learning to achieve autonomy. It is therefore a challenge to ensure dependability of an autonomous system and guarantee that the risks associated with the system are acceptable. The course will introduce modeling, verification and analysis techniques for achieving dependability of autonomous systems.
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Content and learning outcomes
Techniques to achieve dependability, safety analysis, derivation of dependability requirements from safety analysis, modelling and verification of safety requirements, safety assurance case, multi-agent systems, emergent behaviour, goal-oriented modelling and verification of safe and reliable multi-agent autonomous systems, evolutionary algorithms and learning algorithms for mission planning and navigation, safety of mission planning.
Intended learning outcomes
After passing the course, the student shall be able to
- describe dependability attributes formally
- specify dynamic behaviour of autonomous systems and their properties
- use risk assessment and safety analysis techniques to define dependability requirements
- model and verify autonomous systems by means of automatic tools
in order to
- be able to work with autonomous safety critical systems in research and/or development
- be able to identify risks in connection with autonomous systems and use modelling, verification and security techniques to prevent them.
Literature and preparations
- Knowledge and skills in programming, at least 6 higher education credits, equivalent to completed course DD1331/DD1310/DD1311/DD1312/DD1314/DD1315/DD1316/DD1318/DD1321/DD100N/ID1018.
- Knowledge in algorithms and data structures, at least 6 higher education credits, equivalent to completed course DD1320/DD1321/DD1325/DD1327/DD1338/DD2325/ID1020/ID1021.
- Knowledge in mathematics equivalent to at least 22.5 higher education credits.
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
- LAB2 - Laboratory work, 6.5 credits, grading scale: A, B, C, D, E, FX, F
- QUI1 - Digital quiz, 1.0 credits, grading scale: P, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.Course web DD2528
Main field of study
In this course, the EECS code of honor applies, see: